A Computational Systems Analyses to Identify Biomarkers and Mechanistic Link in Psoriasis and Cutaneous Squamous Cell Carcinoma.

Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer.

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

Genome-Wide Profiling of Polyadenylation Events in Maize Using High-Throughput Transcriptomic Sequences.

Polyadenylation is an essential post-transcriptional modification of eukaryotic transcripts that plays critical role in transcript stability, localization, transport, and translational efficiency. About 70% genes in plants contain alternative polyadenylation (APA) sites. Despite availability of vast amount of sequencing data, to date, a comprehensive map of the polyadenylation events in maize is not available. Here, 9.48 billion RNA-Seq reads were analyzed to characterize 95,345 Poly(A) Clusters (PAC) in 23,705 (51%) maize genes. Of these, 76% were APA genes. However, most APA genes (55%) expressed a dominant PAC rather than favoring multiple PACs equally. The lincRNA genes with PACs were significantly longer in length than the genes without any PAC and about 48% genes had APA sites. Heterogeneity was observed in 52% of the PACs supporting the imprecise nature of the polyadenylation process. Genomic distribution revealed that the majority of the PACs (78%) were located in the genic regions. Unlike previous studies, large number of PACs were observed in the intergenic (n = 21,264), 5'-UTR (735), CDS (2,542), and the intronic regions (12,841). The CDS and introns with PACs were longer in length than without PACs, whereas intergenic PACs were more often associated with transcripts that lacked annotated 3'-UTRs. Nucleotide composition around PACs demonstrated AT-richness and the common upstream motif was AAUAAA, which is consistent with other plants. According to this study, only 2,830 genes still maintained the use of AAUAAA motif. This large-scale data provides useful insights about the gene expression regulation and could be utilized as evidence to validate the annotation of transcript ends.